1. Field of the Invention
The present invention relates to methods and apparatus for creating a connection matrix to be used for syntactic processing such as automatic speech recognizer and word processors.
2. Description of the Related Art
Recently systems such as speech automatic speech recognizers and word processors employing syntactic processing techniques have been widely used.
The language processing section of a automatic speech recognizer has usually a function that stores information about words (lexicon), and a function that estimates likelihood of correctness of a sequence of words by referring to a connection matrix. If the likelihood of a sequence is defined as a probability, which takes a value between 0 and 1, then the creation of a connection matrix becomes easy, and the following conventional example is based on this definition. The probability, or likelihood, that the word w(i) follows the sequence of words w(1).multidot.w(2) . . . w(i-1) is hereafter denoted by P{w(i) .vertline. w(1).multidot.w(2) . . . w(i-1)}. If the number of unique words is M, then the number of unique sequences of w(1).multidot.w(2) . . . w(i-1) is M.sup.i-1. Therefore, it is almost impossible to register all probabilities for possible different sequences. However, by the (N+l)-gram approximation of the following formula (1), the number of registered probabilities can be limited to M.sup.N+1. EQU P{w(i) .vertline. w(1).multidot.w(2) . . . w(i-1)}.apprxeq.P{w(i) .vertline. w(i-N).multidot.w(i-N+1) . . . w(i-1)} (1)
FIG. 6 shows a block diagram of conventional apparatus for creating a connection matrix. In FIG. 6, a reference numeral 111 denotes a learning data storage section, which gathers and stores various sequences of words beforehand. 112 denotes a likelihood calculation section that obtains the transition likelihood or probability P{w(i) .vertline. w(i-N).multidot.w(i-N+1) . . . w(i-1)} for each sequence w(i-N).multidot.w(i-N+1) . . . w(i-1) using data stored in the storage section 111. 113 denotes a connection matrix, which is the set of all transition probabilities obtained by the section 112 and becomes the output of this apparatus.
The conventional apparatus organized as above obtains the transition likelihood from the sequence w(i-N).multidot.w(i-N+1) . . . w(i-1) to the symbol w(i) by calculating the following formula (2). EQU P{w(i) .vertline. w(i-N).multidot.w(i-N+1) . . . w(i-1)}=n{w(i-N) . . . w(i-1).multidot.w(i)}/n{w(i-N) . . . w(i-1)} (2)
Here, n{w(i-N) . . . w(i-1).multidot.w(i)} is the total number of sequences w(i-N) . . . w(i-1).multidot.w(i) contained in the learning data stored in the storage section 111, and n{w(i-N) . . . w(i-1)} is the total number of sequences w(i-N) . . . w(i-1) contained in the learning data stored in the storage section 111.
Although the above method reduces the total number of registered likelihoods to M.sup.N+1, it still can not register all likelihoods if it raises the number M.sup.N+1 in order to increase the accuracy of the estimate.